Embodiments of the inventive subject matter generally relate to the field of population-based optimization, and, more particularly, to performing constraint compliant crossovers in population-based optimization simulations.
Population-based optimization algorithms are used to find solutions to optimization problems by starting with an initial set of random candidate solutions (e.g., provided by a user, randomly generated, etc.) and iteratively analyzing and modifying the candidate solutions, according to an objective function, until reaching a satisfactory solution. Population-based optimization algorithms may also be referred to as metahueristic optimization algorithms, combinatorial optimization algorithms, soft-computing algorithms, etc. For instance, one type of population-based optimization algorithm is an evolutionary algorithm. An evolutionary algorithm uses biological techniques loosely based on biological evolution, reproduction, mutation, recombination, and natural selection to find solutions to optimization problems. Simulations that implement evolutionary algorithms act upon populations, such that individuals in a population represent candidate solutions to an optimization problem. The candidate solutions are evaluated for fitness (i.e., evaluated according to a fitness function) and the population “evolves” as successive generations of the population are selected/generated and modified loosely based on the biological techniques. As the population evolves, overall fitness of the population tends to increase. A solution to the optimization problem is found when the overall fitness of the population has reached a satisfactory level, or in other words, when the fitness function, or other objective function, evaluates to an optimal solution. Simulations based on population-based optimization algorithms, such as evolutionary algorithms, can perform well for finding solutions to problems in engineering, biology, economics, robotics, etc. because objective functions can be tailored to fit the problems.